Mapping extreme hot temperatures in Europe and their evolutions: sensitivity to data choices

2020 ◽  
Author(s):  
Sylvie Parey ◽  
Paul-Antoine Michelangeli
Keyword(s):  
Climate Models ◽  
Extreme Temperature ◽  
Weather Conditions ◽  
Value Theory ◽  
Electricity Sector ◽  
Climate Assessment ◽  
Return Levels ◽  
Mean And Variance ◽  
Temperature Levels ◽  
The Impact

<p>Electricity generation and demand is highly dependent on the weather conditions and especially temperature. Ongoing climate change has already modified the very hot extremes in Europe, and this is projected to continue in the future. The anticipation of the necessary adaptations in the electricity sector necessitates information on the possible extreme levels susceptible to occur in the next decades or further future periods. This study aims at comparing different ways of producing maps of extreme temperature levels for different future periods. Extreme temperatures are defined here as an example as 20-year Return Levels, that is temperatures reached or exceeded on average once in 20 years over the considered period. The computation of the Return Levels is based on the methodology described in Parey et al. 2019, which consists in applying the statistical extreme value theory to a standardized variable. It can be proven that the extremes of this variable can be considered as stationary. Then, the changes in mean and variance of the summer temperature projected by different climate models from the CMIP5 archive can be used to derive Return Levels for any selected future period.</p><p>Producing maps necessitates the use of a dataset with a large geographical coverage over Europe. Such datasets are typically gridded, either based on spatial interpolations of station records or on reanalysis products. However, both spatial interpolation and model assimilation tend to smooth the local highest values. Thus, in order to analyze the impact of such smoothing, the Return Levels computed in the same way from different datasets: the European Climate Assessment and dataset station data, the gridded EOBS database or the ERA5-Land database are computed and compared for different future periods.</p>

scholarly journals Early heat waves over Italy and their impacts on durum wheat yields

2015 ◽  
Vol 15 (7) ◽  
pp. 1631-1637 ◽  
Author(s):  
G. Fontana ◽  
A. Toreti ◽  
A. Ceglar ◽  
G. De Sanctis
Keyword(s):  
Durum Wheat ◽  
Mediterranean Region ◽  
Heat Waves ◽  
Extreme Temperature ◽  
Weather Conditions ◽  
Wheat Production ◽  
Arable Crop ◽  
Production Areas ◽  
The Impact ◽  
Concurrent Events

Abstract. In the last decades the Euro-Mediterranean region has experienced an increase in extreme temperature events such as heat waves. These extreme weather conditions can strongly affect arable crop growth and final yields. Here, early heat waves over Italy from 1995 to 2013 are identified and characterised and their impact on durum wheat yields is investigated. As expected, results confirm the impact of the 2003 heat wave and highlight a high percentage of concurrence of early heat waves and significant negative yield anomalies in 13 out of 39 durum wheat production areas. In south-eastern Italy (the most important area for durum wheat production), the percentage of concurrent events exceeds 80 %.

Accounting for Seasonality in Extreme Sea Level Estimation

2021 ◽  
Author(s):  
Eleanor D'Arcy
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Flood Risk ◽  
Storm Surges ◽  
Value Theory ◽  
Return Level ◽  
Geophysical Research ◽  
Coastal Flood ◽  
Return Levels ◽  
The Impact

<p>Storm surges pose an increasing risk to coastline communities. These events, combined with high tide, can result in coastal flooding. To reduce the impact of storm surges, an accurate estimate of coastal flood risk is necessary. Specifically, estimates are required for the return level of sea levels (still water), which is the level with annual exceedance probability <em>p</em>. This estimate is used as an input to determine the height for a coastal defence, such as a sea wall. The return level estimation requires statistical analysis based on extreme value theory, as we need to know about the frequency of events that are more extreme than those previously observed.</p><p>Large storm surges exhibit seasonality, they are typically at their worst in the winter and least extreme in the summer. This seasonal pattern differs from that of the tide, whose seasonality is driven astronomically, resulting in tidal peaks at the spring and autumn equinoxes. Hence, the worst levels of these two components of still water level are likely to peak at different times in the year, and so statistical methods that treat them as independent variables are likely to over-estimate return levels.</p><p>We focus on the skew surge: the difference between the observed and predicted high water within a tidal cycle. Williams et al. (2016) show that tide and skew surge are independent conditional on the time of year. Batstone et al. (2013) used this property to derive estimates used for UK coastal flood defences. They used generalised Pareto distributions for the skew surge tail but did not account for the separate seasonality of tide and skew surge.</p><p>This work aims to model how the distribution of skew surges changes over a year and we combine our results with the known seasonality of tides to derive estimates of still water level return levels. We compare our results with the Batstone et al. (2013) approach at a few locations on the UK coastline.</p><p>References:</p><p>Batstone, C., Lawless, M., Tawn, J., Horsburgh, K., Blackman, D., McMillan, A., Worth, D., Laeger, S. and Hunt, T., 2013. A UK best-practice approach for extreme sea-level analysis along complex topographic coastlines. Ocean Engineering, 71, pp.28-39.</p><p>Williams, J., Horsburgh, K.J., Williams, J.A. and Proctor, R.N., 2016. Tide and skew surge independence: New insights for flood risk. Geophysical Research Letters, 43(12), pp.6410-6417.</p>

scholarly journals Power Quality Performance of Fast-Charging under Extreme Temperature Conditions

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2635 ◽  
Author(s):  
Alexandre Lucas ◽  
Germana Trentadue ◽  
Harald Scholz ◽  
Marcos Otura
Keyword(s):  
Power Factor ◽  
High Harmonic ◽  
Extreme Temperature ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Quality Performance ◽  
Fast Charging ◽  
Standby Power ◽  
Temperature Levels ◽  
The Impact

Exposing electric vehicles (EV) to extreme temperatures limits its performance and charging. For the foreseen adoption of EVs, it is not only important to study the technology behind it, but also the environment it will be inserted into. In Europe, temperatures ranging from −30 °C to +40 °C are frequently observed and the impacts on batteries are well-known. However, the impact on the grid due to the performance of fast-chargers, under such conditions, also requires analysis, as it impacts both on the infrastructure’s dimensioning and design. In this study, six different fast-chargers were analysed while charging a full battery EV, under four temperature levels (−25 °C, −15 °C, +20 °C, and +40 °C). The current total harmonic distortion, power factor, standby power, and unbalance were registered. Results show that the current total harmonic distortion (THDI) tended to increase at lower temperatures. The standby consumption showed no trend, with results ranging from 210 VA to 1650 VA. Three out of six chargers lost interoperability at −25 °C. Such non-linear loads, present high harmonic distortion, and, hence, low power factor. The temperature at which the vehicle’s battery charges is crucial to the current it withdraws, thereby, influencing the charger’s performance.

scholarly journals Extreme Temperatures and Firm-Level Stock Returns

2021 ◽  
Vol 18 (4) ◽  
pp. 2004
Author(s):  
Jingbin He ◽  
Xinru Ma
Keyword(s):  
Stock Returns ◽  
Fixed Effects ◽  
Extreme Temperature ◽  
Weather Conditions ◽  
Ordinary Least Squares ◽  
Extreme Temperatures ◽  
Least Squares Regression ◽  
Firm Level ◽  
Induced Mood ◽  
The Impact

By linking stock returns with weather conditions from 2007 to 2019 in China, we study how firm-level stock returns react to extreme temperatures. Based on a multivariate ordinary least squares regression model with fixed effects, empirical results show that firm-level stock returns decrease with exposure to extreme temperatures. We further explore the heterogeneity in the temperature-return relation to enrich our understanding of the economic mechanism behind it. The impact of extreme temperatures on abnormal stock returns is more pronounced in smaller, younger, more volatile, less profitable firms and firms with more intangible assets. The results indicate that the investor mood likely plays a role in the extreme temperature effect. The impact of extreme temperatures holds after addressing a series of concerns. Overall, our paper provides additional firm-level evidence on the environment-induced mood effect in the stock market.

scholarly journals Early heat waves over Italy and their impacts on durum wheat yields

2015 ◽  
Vol 3 (5) ◽  
pp. 2953-2973 ◽  
Author(s):  
G. Fontana ◽  
A. Toreti ◽  
A. Ceglar ◽  
G. De Sanctis
Keyword(s):  
Durum Wheat ◽  
Mediterranean Region ◽  
Heat Waves ◽  
Extreme Temperature ◽  
Weather Conditions ◽  
Wheat Production ◽  
Arable Crop ◽  
Production Areas ◽  
The Impact ◽  
Concurrent Events

Abstract. In the last decades the Euro-Mediterranean region has experienced an increase in extreme temperature events such as heat waves. These extreme weather conditions can strongly affect arable crop growth and final yields. Here, early heat waves over Italy from 1995 to 2013 are identified and characterised and their impact on durum wheat yields is investigated. As expected, results confirm the impact of the 2003 heat waves and highlight a high percentage of concurrence of early heat waves and significant negative yield anomalies in 13 out of 39 durum wheat production areas. In south-eastern Italy (the most important area for durum wheat production), the percentage of concurrent events exceeds 80%.

scholarly journals A Model for Nighttime Minimum Temperatures

2014 ◽  
Vol 27 (19) ◽  
pp. 7207-7229 ◽  
Author(s):  
Debbie J. Dupuis
Keyword(s):  
Minimum Temperature ◽  
Climate Models ◽  
Marginal Distribution ◽  
Complex Dynamics ◽  
Extreme Temperature ◽  
Considerable Change ◽  
Conditional Heteroskedasticity ◽  
Residual Series ◽  
Maximum Temperatures ◽  
Temperature Levels

Abstract The southwestern United States has experienced some of the most important increases in nighttime minimum temperatures over the last 60 yr, and climate models are projecting more of the same to the end of the century. As climate, geography, and population density vary considerably over the area, very diverse extreme temperature levels and dynamics are observed. It is shown how nighttime minimum temperatures over the 1950–2009 period exhibit more complex dynamics than daytime maximum temperatures. The author studies nighttime minimum temperature series from 12 locations and presents one model capable of capturing all the features of the data at each location. The time series preprocessing model normalizes seasonal shocks by daily and yearly volatility components before modeling the residual volatility as an exponential generalized autoregressive conditional heteroskedasticity [EGARCH(1, 1)] process with seasonal autoregressive structure to account for the presence of nonlinear and seasonal linear dependence, respectively, in the residual series. An exceedance over high thresholds approach is then used to model the tail of the distribution of scaled residuals from the preprocessing model. The resulting marginal distribution of nighttime minimum temperature at each location is then examined to see how it has changed in mean, scale, and shape, respectively, over the 60-yr period. Changes at the 12 locations vary considerably: many locations have seen considerable change in some or all of the three parameters, while two locations have experienced little or no change.

scholarly journals A Study Protocol to Determine Heat-Related Health Impacts among Primary Schoolchildren in South Africa

2020 ◽  
Vol 17 (15) ◽  
pp. 5531
Author(s):  
Shalin Bidassey-Manilal ◽  
Caradee Yael Wright ◽  
Thandi Kapwata ◽  
Joyce Shirinde
Keyword(s):  
South Africa ◽  
Primary Schools ◽  
Climate Models ◽  
Neuropsychological Test ◽  
Panel Study ◽  
Weather Conditions ◽  
Well Being ◽  
Mitigation Measures ◽  
Hot Weather ◽  
The Impact

Climate models predict that the global average temperature of Earth will rise in the future. Studies show that high classroom temperatures can affect the ability of the student to learn and function. It is important to understand the impact that heat will have on the health, wellbeing, and academic performance of learners, as they spend a significant amount of time in classrooms compared to any other environment. A follow-up panel study among 20 public primary schools in the Gauteng province (South Africa) will be carried out, in which Grade 4 learners will be selected to complete an hourly heat-health symptom questionnaire. A Cambridge Neuropsychological Test Automated Battery (CANTAB) test will be used to determine their memory and attention span. A nursing practitioner will measure body weight, height, and temperature. Lascar data loggers will be used to measure indoor classroom temperature. School principals will complete a questionnaire on existing school coping mechanisms and policies in place that help deal with hot weather conditions. This is the first study to quantitatively assess the effects of heat on learners’ health, well-being and school performance in South Africa. The outcomes of this study will enable policymakers and public officials to develop appropriate school heat adaptation and mitigation measures and will assist in channeling their resources where it is most needed.

scholarly journals Unpacking future climate extremes and their sectoral implications in western Nepal

2021 ◽  
Vol 168 (1-2) ◽  
Author(s):  
Dipesh Chapagain ◽  
Sanita Dhaubanjar ◽  
Luna Bharati
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Climate Extremes ◽  
Climate Indices ◽  
Climate Projections ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events ◽  
The Impact ◽  
Precipitation Events

AbstractExisting climate projections and impact assessments in Nepal only consider a limited number of generic climate indices such as means. Few studies have explored climate extremes and their sectoral implications. This study evaluates future scenarios of extreme climate indices from the list of the Expert Team on Sector-specific Climate Indices (ET-SCI) and their sectoral implications in the Karnali Basin in western Nepal. First, future projections of 26 climate indices relevant to six climate-sensitive sectors in Karnali are made for the near (2021–2045), mid (2046–2070), and far (2071–2095) future for low- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) using bias-corrected ensembles of 19 regional climate models from the COordinated Regional Downscaling EXperiment for South Asia (CORDEX-SA). Second, a qualitative analysis based on expert interviews and a literature review on the impact of the projected climate extremes on the climate-sensitive sectors is undertaken. Both the temperature and precipitation patterns are projected to deviate significantly from the historical reference already from the near future with increased occurrences of extreme events. Winter in the highlands is expected to become warmer and dryer. The hot and wet tropical summer in the lowlands will become hotter with longer warm spells and fewer cold days. Low-intensity precipitation events will decline, but the magnitude and frequency of extreme precipitation events will increase. The compounding effects of the increase in extreme temperature and precipitation events will have largely negative implications for the six climate-sensitive sectors considered here.

scholarly journals The Value of High-Resolution Met Office Regional Climate Models in the Simulation of Multihourly Precipitation Extremes

2014 ◽  
Vol 27 (16) ◽  
pp. 6155-6174 ◽  
Author(s):  
Steven C. Chan ◽  
Elizabeth J. Kendon ◽  
Hayley J. Fowler ◽  
Stephen Blenkinsop ◽  
Nigel M. Roberts ◽  
...  
Keyword(s):  
High Resolution ◽  
Time Scales ◽  
Return Period ◽  
Extreme Events ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Value Theory ◽  
Precipitation Extremes ◽  
Return Levels

Abstract Extreme value theory is used as a diagnostic for two high-resolution (12-km parameterized convection and 1.5-km explicit convection) Met Office regional climate model (RCM) simulations. On subdaily time scales, the 12-km simulation has weaker June–August (JJA) short-return-period return levels than the 1.5-km RCM, yet the 12-km RCM has overly large high return levels. Comparisons with observations indicate that the 1.5-km RCM is more successful than the 12-km RCM in representing (multi)hourly JJA very extreme events. As accumulation periods increase toward daily time scales, the erroneous 12-km precipitation extremes become more comparable with the observations and the 1.5-km RCM. The 12-km RCM fails to capture the observed low sensitivity of the growth rate to accumulation period changes, which is successfully captured by the 1.5-km RCM. Both simulations have comparable December–February (DJF) extremes, but the DJF extremes are generally weaker than in JJA at daily or shorter time scales. Case studies indicate that “gridpoint storms” are one of the causes of unrealistic very extreme events in the 12-km RCM. Caution is needed in interpreting the realism of 12-km RCM JJA extremes, including short-return-period events, which have return values closer to observations. There is clear evidence that the 1.5-km RCM has a higher degree of realism than the 12-km RCM in the simulation of JJA extremes.

Unprecedented summertime daily rainfall across the UK

2021 ◽  
Author(s):  
Chris Kent ◽  
Nick Dunstone ◽  
Simon Tucker ◽  
Adam Scaife ◽  
Elizabeth Kendon ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Rare Events ◽  
Daily Rainfall ◽  
Value Theory ◽  
Water Flooding ◽  
Large Ensemble ◽  
Rainfall Events ◽  
Return Levels ◽  
The Uk

<p>The UNSEEN (UNprecedented Simulated Extremes using ENsembles) method involves using a large ensemble of initialised climate model simulations to increase the sample size of rare events. In this work we extended UNSEEN to focus on intense summertime daily rainfall events. Specifically, plausible extreme rainfall scenarios were developed to help understand potential surface water flooding impacts, and ultimately better inform flood management and resilience across the UK. To help address modelling limitations a large ensemble of simulations from two climate models were used; an initialised 25km global model that uses parametrized convection, and a dynamically downscaled 2.2km model that uses explicit convection. Climate model fidelity was assessed using a regional pooling technique based on extreme value theory. Across much of the UK both models are indistinguishable from the observations in terms of the statistical characteristics which govern the magnitude of very rare return periods. The UNSEEN analysis provides new estimates of plausible extreme return levels (i.e. 1-in-1000 year) across the UK and can reduce uncertainty in the expected frequency of very rare events by 50-70% compared to estimates using observations alone. These results enable suitable observed rainfall profiles to be uplifted to plausible extreme return levels, which can then be used within regional hydrological models to stress test surface flooding scenarios. The annual chance of unprecedented daily rainfall events in the current climate is also quantified, and found to be up to 5% (1-in-20 year return level) for many grid cells across southern parts of the UK. Finally, a significant benefit of the UNSEEN approach over purely statistical emulators is the use of dynamical climate models which allow the large-scale dynamical drivers of extreme daily summertime rainfall to be assessed.</p>

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